Specification Of Data Types Research Articles

A latent class based imputation method under Bayesian quantile regression framework using asymmetric Laplace distribution for longitudinal medication usage data with intermittent missing values

ABSTRACTEvaluating the association between diseases and the longitudinal pattern of pharmacological therapy has become increasingly important. However, in many longitudinal studies, self-reported medication usage data collected at patients’ follow-up visits could be missing for various reasons. These pieces of missing or inaccurate/untenable information complicate determining the trajectory of medication use and its complete effects for patients. Although longitudinal models can deal with specific types of missing data, inappropriate handling of this issue can lead to a biased estimation of regression parameters especially when missing data mechanisms are complex and depend upon multiple sources of variation. We propose a latent class-based multiple imputation (MI) approach using a Bayesian quantile regression (BQR) that incorporates cluster of unobserved heterogeneity for medication usage data with intermittent missing values. Findings from our simulation study indicate that the proposed method performs better than traditional MI methods under certain scenarios of data distribution. We also demonstrate applications of the proposed method to data from the Prospective Study of Outcomes in Ankylosing Spondylitis (AS) cohort when assessing an association between longitudinal nonsteroidal anti-inflammatory drugs (NSAIDs) usage and radiographic damage in AS, while the longitudinal NSAID index data are intermittently missing.

New protocols of cognitive data management and sharing in cloud computing

SummaryIn this paper, we shall discuss new classes of cognitive systems dedicated the tasks of dividing and managing shared information not only in simple horizontal structures but also primarily in vertical structures. An example of such a structure is a computer cloud where, from various levels of access to information, one can execute the management process, both with respect to the entire information resources, as well as to individual pieces of data, including shared information. A special type of information is a service, which, similar to various types of data, is subject to the concealment process. In this work, we shall present universal algorithms for shared data management, such that can be used in concealment protocols both for data and services. Due to the above, this paper will treat the term “service” as a special type of data, constituting the basis of their sharing and management processes. Moreover, we shall determine the impact of the proposed algorithms on the enhancement of data/service management processes in Cloud Computing as well as the impact of the service management and data sharing processes in Could Computing on decision‐taking processes in systems supporting the management processes.

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

A comparative study of WhatsApp forensics tools

With the increasing number of mobile phones and mobile applications, there is a noticeable rise in cybercrimes. Hence, an urgent need for mobile forensics. Before starting investigation, the investigator should choose one of the acquisition types; physical acquisition, logical acquisition or manual acquisition. The current mobile acquisition tools use these methods to produce an image of the entire mobile content, files of specific datatypes, or data of a certain application. Unfortunately, the resultant output does not facilitate investigating cases related to specific mobile application, since the tool might acquire more than what is needed which requires investigators to filter data manually, or acquire all the application’s data without sufficient analysis. Both cases are effort and time consuming. This study analyzes and compares currently available forensics tools that are designed to extract WhatsApp data only. The comparative study is based on two aspects; National Institute of Standards and Technology (NIST) Mobile Device Tool Test Assertions and researchers’ requirements. The results of the comparative study showed a shortage in the current WhatsApp forensics tools as they do not satisfy all NIST Test Assertions. Additionally, several researchers’ requirements such as: creating projects, comprehensive analysis, applying filters and validating the extracted files, were not met in the studied tools.

A scheme for the sticky policy representation supporting secure cyber-threat intelligence analysis and sharing

PurposeThis paper aims to propose a structured threat information expression (STIX)-based data representation for privacy-preserving data analysis to report format and semantics of specific data types and to represent sticky policies in the format of embedded human-readable data sharing agreements (DSAs). More specifically, the authors exploit and extend the STIX standard to represent in a structured way analysis-ready pieces of data and the attached privacy policies.Design/methodology/approachThe whole scheme is designed to be completely compatible with the STIX 2.0 standard for cyber-threat intelligence (CTI) representation. The proposed scheme will be implemented in this work by defining the complete scheme for representing an email, which is more expressive than the standard one defined for STIX, designed specifically for spam email analysis.FindingsMoreover, the paper provides a new scheme for general DSA representation that has been practically applied for the process of encoding specific attributes in different CTI reports.Research limitations/implicationsBecause of the chosen approach, the research results may have limitations. Specifically, current practice for entity recognition has the limitation that was discovered during the research. However, its effect on process time was minimized and the way for improvement was proposed.Originality/valueThis paper has covered the existing gap including the lack of generality in DSA representation for privacy-preserving analysis of structured CTI. Therefore, the new model for DSA representation was introduced, as well as its practical implementation.

Parallel graph-based anomaly detection technique for sequential data

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Semantic Web Services: An Empirical Methodology to Security

In today's modern era, the Semantic Web is progressing at a tremendous speed. The Semantic Web services have opened new possibilities through which any one can avail various services. But if these technologies are not properly protected, then their use can put users in danger. Many people have to face difficulty in assessing the security hazards allied with Semantic Web services. Many Web services such as e-commerce stores various types of data on their database servers. These database servers are distributed across the globe. The e-commerce may use the unalike assortments of database for storing different information. Web services such as e-commerce have to handle assorted types of data. Unalike Web services like e-commerce have to handle assorted types of data. A single database program will not be suitable for storing and processing mixed types of data as it will increase the processing overhead and ultimately reduce the performance of the web service. So there is need arises to define an apposite subclass of databases for each document type.This paper insinuates a set of databases for each type of documents stored on an e-commerce platform. An exertion has been made to define a proper set of database programs for a web service such as e-commerce that adequately manages the requirements of a particular type of data. The tryouts have been steered for an e-commerce Web service for weighing the efficacy of the proposed approach. The results shows that if well-structured database has been used for a Web service such as e-commerce then response can be very quick and chances of packet loss will also be very low. If network attacks such as DoS and man-in-the-middle attack exist within the request, the performance of the Web service severely affected. A network attack may lead to the more chances of packet loss. The paper also delineates the impact of network attacks on web service performance.

A mixed-methods study of challenges experienced by clinical teams in measuring improvement

ObjectiveMeasurement is an indispensable element of most quality improvement (QI) projects, but it is undertaken to variable standards. We aimed to characterise challenges faced by clinical teams in undertaking measurement...

Powerful statistical method to detect disease‐associated genes using publicly available genome‐wide association studies summary data

Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in -cell function, glucose homeostasis, and lipids traits.

PrivateSQL

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.

HTML FORM GENERATOR BASED ON TABLE USING OBJECT ORIENTED PROGRAMMING

To insert new data into a database table using a web-based application, a graphical user interface in the form of HTML Form is required. Each table field / attribute requires an appropriate form control in order to minimize data errors that will be entered. There is a relation between the data type of a field in the table with the type of form control to be used, therefore the graphical user interface in the form of HTML Form can be created automatically. There are various control forms of HTML in the form of tags, generally in the form of input tags. What distinguishes the form control from one to another is the attribute: type, size, value therefore to determine the type and content of form controls can be given through parameters. HTML Form can be regarded as an object which has many other objects in the form of form controls. Object Oriented Programming (OOP) paradigm it can be implemented to build HTML Form. Through meta data from a table, it will be able to obtain the appropriate HTML form control, but for each specific data type it can have the appropriate form control candidate, therefore before Form HTML is created by the generator, there should be a little user intervention to get the interface The desired HTML form.

HTML FORM GENERATOR BASED ON TABLE USING OBJECT ORIENTED PROGRAMMING

To insert new data into a database table using a web-based application, a graphical user interface in the form of HTML Form is required. Each table field / attribute requires an appropriate form control in order to minimize data errors that will be entered. There is a relation between the data type of a field in the table with the type of form control to be used, therefore the graphical user interface in the form of HTML Form can be created automatically. There are various control forms of HTML in the form of tags, generally in the form of input tags. What distinguishes the form control from one to another is the attribute: type, size, value therefore to determine the type and content of form controls can be given through parameters. HTML Form can be regarded as an object which has many other objects in the form of form controls. Object Oriented Programming (OOP) paradigm it can be implemented to build HTML Form. Through meta data from a table, it will be able to obtain the appropriate HTML form control, but for each specific data type it can have the appropriate form control candidate, therefore before Form HTML is created by the generator, there should be a little user intervention to get the interface The desired HTML form.

Scientific engagement and the development of marine aquaculture in Santa Catarina, southern Brazil

Science-based marine aquaculture, or mariculture, is expanding around the world. Nonetheless, how scientists engage in mariculture planning, and why particular types of data are used to inform development decision-making, is less clear. In the southern Brazilian state of Santa Catarina, coastal managers and scientists embarked on an ambitious effort to establish shellfish farming and created a thriving mariculture industry. This study draws upon in-depth interviews with scientists, government officials, and shellfish growers to better understand the social forces that affected scientific engagement in mariculture planning in Santa Catarina. From agronomic insights about shellfish growth to microbiological understanding of pathogenic threats to seafood, wide-ranging types of science could inform mariculture planning. Our data show marked differences in 1) the involvement of scientists based on their disciplinary expertise and 2) the use of production versus impact or risk-related data to support decision-making. Utilizing conceptual insights from sociological study of science and institutions, we show how normative, cultural-cognitive, and regulative forces influence both scientists’ involvement in planning and the use of scientific data to inform mariculture-related decisions. Most notably, asymmetries appear in the effects of norms related to methodological practices among scientists focused on enhancing shellfish production versus those investigating potential health and environmental concerns. Cultural differences among scientists from different disciplines also affected their inclination to collaborate with government officials and growers. Finally, ambiguities in mariculture-related regulations led to the differential involvement of scientists, in particular hindering investigations focused on seafood safety and public health. These results illustrate that social forces influence how science is practiced and that this, in turn, shapes the course of science-based mariculture development. Given their key social role, broader sociological investigation of scientists as social actors could provide valuable insights to those seeking to ensure coastal development is both socially and ecologically sustainable.

Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique

The increased use of cyber-enabled systems and Internet-of-Things (IoT) led to a massive amount of data with different structures. Most big data solutions are built on top of the Hadoop eco-system or use its distributed file system (HDFS). However, studies have shown inefficiency in such systems when dealing with today's data. Some research overcame these problems for specific types of graph data, but today's data are more than one type of data. Such efficiency issues may lead to large-scale problems, including larger space requirements in data centers, and waste in resources (like power consumption), that in turn lead to environmental problems (such as more carbon emission) [1] , as per scholars. We propose a data-aware module for the Hadoop eco-system. We also propose a distributed encoding technique for genetic algorithms efficient data processing. Our framework allows Hadoop to manage the distribution of data and its placement based on cluster analysis of the data itself. We are able to handle a broad range of data types as well as optimize query time and resource usage. We performed experiments on multiple datasets generated via LUBM (Lehigh University Benchmark) and reported results along with performance analysis.

Designing and making commercially driven integrated dictionary portals: the Diccionarios Valladolid-UVa

Integrated dictionary portals such as the Diccionarios Valladolid-UVa are reference tools prepared by the same team and equipped with Dictionary Writing Systems that consists of editors, databases, Web interfaces, management tools, and a kind of dictionary grammar which specifies a common structure for all the dictionaries of the portal. They were designed and are being constructed by the joint efforts of lexicographers, IT and domain experts, and a language technology Danish company. The project started in the year 2014, initially following Bergenholtz’s works in several commercially oriented Danish dictionaries. It has now been accommodated to new lexicographic insights and technological developments, and is on the road to “producing” around 28 integrated dictionaries as well as specific search devices, e.g. for searching Spanish Americanisms or specific translations of specific expressions. The project is based on the tenets of the Function Theory of Lexicography, Christensen’s concept of disruptive innovation, and assumptions on the economic potential of lexicographic data. For reasons of space and target readership, this paper will be mostly concerned with presenting the concept of typological individualization, i.e. offering specific data types for specific user types in specific situation types.

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

An integrative association method for omics data based on a modified Fisher's method with application to childhood asthma.

The development of high-throughput biotechnologies allows the collection of omics data to study the biological mechanisms underlying complex diseases at different levels, such as genomics, epigenomics, and transcriptomics. However, each technology is designed to collect a specific type of omics data. Thus, the association between a disease and one type of omics data is usually tested individually, but this strategy is suboptimal. To better articulate biological processes and increase the consistency of variant identification, omics data from various platforms need to be integrated. In this report, we introduce an approach that uses a modified Fisher’s method (denoted as Omnibus-Fisher) to combine separate p-values of association testing for a trait and SNPs, DNA methylation markers, and RNA sequencing, calculated by kernel machine regression into an overall gene-level p-value to account for correlation between omics data. To consider all possible disease models, we extend Omnibus-Fisher to an optimal test by using perturbations. In our simulations, a usual Fisher’s method has inflated type I error rates when directly applied to correlated omics data. In contrast, Omnibus-Fisher preserves the expected type I error rates. Moreover, Omnibus-Fisher has increased power compared to its optimal version when the true disease model involves all types of omics data. On the other hand, the optimal Omnibus-Fisher is more powerful than its regular version when only one type of data is causal. Finally, we illustrate our proposed method by analyzing whole-genome genotyping, DNA methylation data, and RNA sequencing data from a study of childhood asthma in Puerto Ricans.

Treatment Level and Store Level Analyses of Healthcare Data

The presented research discusses general approaches to analyze and model healthcare data at the treatment level and at the store level. The paper consists of two parts: (1) a general analysis method for store-level product sales of an organization and (2) a treatment-level analysis method of healthcare expenditures. In the first part, our goal is to develop a modeling framework to help understand the factors influencing the sales volume of stores maintained by a healthcare organization. In the second part of the paper, we demonstrate a treatment-level approach to modeling healthcare expenditures. In this part, we aim to improve the operational-level management of a healthcare provider by predicting the total cost of medical services. From this perspective, treatment-level analyses of medical expenditures may help provide a micro-level approach to predicting the total amount of expenditures for a healthcare provider. We present a model for analyzing a specific type of medical data, which may arise commonly in a healthcare provider’s standardized database. We do this by using an extension of the frequency-severity approach to modeling insurance expenditures from the actuarial science literature.

EJSTK: Building JavaScript virtual machines with customized datatypes for embedded systems

Abstract Using JavaScript as a description language can increase the productivity of application programs on embedded systems. Since JavaScript is a dynamic language, it is important for a JavaScript virtual machine (VM) to efficiently identify the types of first-class values and perform type-based dispatches. For embedded systems, the type-based dispatching code is desired to be compact as well as fast. Although operators of JavaScript are heavily overloaded and capable of accepting a variety of datatypes as operands, all the datatypes are not always necessarily used in a specific program. If some datatypes are never used in this program, a VM for a subset of JavaScript with operators that support limited datatypes suffices. Such a VM is expected to be compact and efficient. In addition, internal representation of datatype of each value may affect performance of type-based dispatching. This paper presents a novel framework that can generate a VM for a subset of JavaScript on the basis of operand specifications and datatype specifications given by the programmer. The operand specifications describe possible operand datatypes for every instruction and the datatype specifications describe adequate internal representations of necessary datatypes for a target program. The generated VM is specialized in the sense that it has efficient and minimum type-based dispatching code for all instructions.

Effect of impact factor and discipline on journal data sharing policies

ABSTRACTData sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.